Large language models (LLMs) have had a significant impact on NLP research and applications. These models have improved performance and revealed new skills across various tasks. There are different types of LLMs, including encoder-only models, decoder-only models, and encoder-decoder models. The exponential growth in model sizes and training datasets has played a crucial role in enhancing the capabilities of LLMs. For example, newer GPT-based models now have hundreds of billions of parameters.
However, training datasets for LLMs often lack openness, especially for the latest state-of-the-art models. This lack of transparency hinders replication of findings and progress in research on hallucination and bias in LLMs. Multilingual learning scenarios face additional challenges due to inadequate multilingual text collections. To address these issues, academics at the University of Oregon and Adobe Research collaborated to develop CulturaX, a massive multilingual dataset. This dataset contains 6.3 trillion tokens in 167 languages and undergoes a rigorous cleaning and deduplication process to ensure high-quality training data for LLMs.
The cleaning and deduplication process of CulturaX involves several steps to eliminate inaccurate information. This includes addressing inaccuracies in language identification, removing poisonous data, and eliminating non-linguistic material.
– CulturaX is the largest open-source multilingual dataset that has been thoroughly cleaned and deduplicated for LLM and NLP applications.
– The dataset provides high-quality data for training LLMs in multiple languages, solving existing problems with current datasets.
– Existing multilingual datasets like mC4 and OSCAR do not meet the requirements for efficiently training LLMs, especially generative models like GPT. They lack document-level fuzzy deduplication and may have inferior language recognition.
– CulturaX’s release by HuggingFace offers opportunities for further research and applications of multilingual LLMs.
To access CulturaX, visit this link: https://huggingface.co/datasets/uonlp/CulturaX.
In conclusion, CulturaX is a valuable resource for training LLMs in multiple languages. The dataset’s thorough cleaning and deduplication process ensure high quality and accuracy. Researchers and practitioners can utilize CulturaX to enhance their understanding of multilingual LLMs and explore new applications.
Check out the paper and dataset for more details. All credit for this research goes to the researchers involved in the project. Don’t forget to join our ML SubReddit, Facebook Community, Discord Channel, and Email Newsletter for the latest AI research news and projects.
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